{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aEJv_ZkwEGba"
   },
   "source": [
    "<center>\n",
    "    <p style=\"text-align:center\">\n",
    "        <img alt=\"phoenix logo\" src=\"https://storage.googleapis.com/arize-assets/arize-logo-white.jpg\" width=\"200\"/>\n",
    "        <br>\n",
    "        <img alt=\"phoenix logo\" src=\"https://storage.googleapis.com/arize-assets/phoenix/assets/phoenix-logo-light.svg\" width=\"200\"/>\n",
    "        <br>\n",
    "        <a href=\"https://arize.com/docs/phoenix/\">Docs</a>\n",
    "        |\n",
    "        <a href=\"https://github.com/Arize-ai/phoenix\">GitHub</a>\n",
    "        |\n",
    "        <a href=\"https://arize-ai.slack.com/join/shared_invite/zt-2w57bhem8-hq24MB6u7yE_ZF_ilOYSBw#/shared-invite/email\">Community</a>\n",
    "    </p>\n",
    "</center>\n",
    "<h1 align=\"center\">Agent Tool Parameter Extraction Classification Evals</h1>\n",
    "\n",
    "The purpose of this notebook is:\n",
    "\n",
    "- to evaluate the performance of an LLM-assisted approach to evaluating agent tool parameter extraction,\n",
    "- to provide an experimental framework for users to iterate and improve on the default agent tool parameter extraction template.\n",
    "\n",
    "## Install Dependencies and Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0la5vhfxEGbb",
    "outputId": "492cf86b-2bfb-49a4-b3d1-b3396fcdab32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: seaborn\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qq \"arize-phoenix-evals>=0.22.0\" \"openai>=1\" ipython matplotlib pycm pyarrow opentelemetry-sdk scikit-learn arize tiktoken nest_asyncio 'httpx<0.28'\n",
    "%pip install \"arize[Datasets]\" opentelemetry-sdk\n",
    "%pip install openai pandas seaborn\n",
    "%pip install \"anthropic>=0.18.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UX492hfvEGbb"
   },
   "source": [
    "ℹ️ To enable async request submission in notebook environments like Jupyter or Google Colab, optionally use `nest_asyncio`. `nest_asyncio` globally patches `asyncio` to enable event loops to be re-entrant. This is not required for non-notebook environments.\n",
    "\n",
    "Without `nest_asyncio`, eval submission can be much slower, depending on your organization's rate limits. Speed increases of about 5x are typical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5N4dMv_1EGbc"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import openai\n",
    "import pandas as pd\n",
    "from pycm import ConfusionMatrix\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gthqcv8xEGbc"
   },
   "source": [
    "## Download Benchmark Dataset\n",
    "\n",
    "We'll evaluate the evaluation system consisting of a few example user prompts, with corresponding tool choices and ground truth answers, in addition to an evaluation prompt template against benchmark datasets of user queries and the corresponding tools called with ground-truth labels. We will be using the Berkely Function-Calling Leaderboard dataset. This dataset is commonly used for Agent applications that call tools as a benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a5mv7s5zPpTu",
    "outputId": "950664fe-06b9-40cb-ab49-12fd2a61aa8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: wget\n"
     ]
    }
   ],
   "source": [
    "!wget -O exec_simple.json https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard/blob/main/BFCL_v3_exec_multiple.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "FW2qxwM2P0sh",
    "outputId": "3cb37a64-a013-4100-9d2e-41826ab98452"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "# Load dataset\n",
    "url = \"https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard/resolve/main/BFCL_v3_exec_multiple.json\"\n",
    "response = requests.get(url)\n",
    "dataset = [json.loads(line) for line in response.text.strip().splitlines()]\n",
    "\n",
    "# Collect all unique tool definitions (these are all the tools the agent\n",
    "# can choose from, as well as their parameters)\n",
    "unique_tools = {}\n",
    "for entry in dataset:\n",
    "    for tool in entry.get(\"function\", []):\n",
    "        if tool[\"name\"] not in unique_tools:\n",
    "            unique_tools[tool[\"name\"]] = tool\n",
    "\n",
    "tool_definitions_text = \"\\n\".join(\n",
    "    f\"{tool['name']}: {tool['description']} | Parameters: {json.dumps(tool['parameters'])}\"\n",
    "    for tool in unique_tools.values()\n",
    ")\n",
    "\n",
    "\n",
    "# Prepare data for evaluation\n",
    "eval_data = []\n",
    "for entry in dataset:\n",
    "    question = entry[\"question\"][0][0][\"content\"]\n",
    "    ground_truths = entry.get(\"ground_truth\", [])\n",
    "    for gt in ground_truths:\n",
    "        eval_data.append(\n",
    "            {\"question\": question, \"tool_call\": gt, \"tool_definitions\": tool_definitions_text}\n",
    "        )\n",
    "\n",
    "df_eval = pd.DataFrame(eval_data)\n",
    "df_eval.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eO7_Lz5iSxnt"
   },
   "source": [
    "## Modify Benchmark Dataset\n",
    "\n",
    "The BFCL Dataset does not have any `negative` examples, i.e. only `question`, `available_tools`, and `ground_truth` are present. In order to accurately benchmark our LLM-as-a-Judge, this code snippet appends some `incorrect` examples to our dataset, by adding 20 rows of data where the `incorrect` tool is selected with correspondingly incorrect parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "TnUiOiwYS31x"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import random\n",
    "import re\n",
    "\n",
    "\n",
    "# Parse tool definitions into a dict: tool_name -> list of required parameters\n",
    "def extract_tool_param_templates(tool_definitions):\n",
    "    tools = {}\n",
    "    pattern = r\"^(\\w+):.*?\\| Parameters: (.*)$\"\n",
    "    for line in tool_definitions.strip().split(\"\\n\"):\n",
    "        match = re.match(pattern, line.strip())\n",
    "        if match:\n",
    "            name, param_json = match.groups()\n",
    "            try:\n",
    "                param_schema = json.loads(param_json)\n",
    "                required = param_schema.get(\"required\", [])\n",
    "                tools[name] = required\n",
    "            except json.JSONDecodeError:\n",
    "                continue\n",
    "    return tools\n",
    "\n",
    "\n",
    "tool_param_templates = extract_tool_param_templates(tool_definitions_text)\n",
    "tool_names = list(tool_param_templates.keys())\n",
    "\n",
    "# Sample 20 wrong examples\n",
    "wrong_examples = []\n",
    "for i in range(20):\n",
    "    row = df_eval.iloc[i]\n",
    "    question = row[\"question\"]\n",
    "    correct_tool_call_str = row[\"tool_call\"]\n",
    "\n",
    "    # Get correct tool name (assume it's before the first '(')\n",
    "    try:\n",
    "        correct_tool_name = correct_tool_call_str.split(\"(\")[0]\n",
    "    except Exception as e:\n",
    "        print(f\"Skipping row {i} due to error: {e}\")\n",
    "        continue\n",
    "\n",
    "    # Get a different tool\n",
    "    incorrect_tools = [tool for tool in tool_names if tool != correct_tool_name]\n",
    "    if not incorrect_tools:\n",
    "        continue\n",
    "\n",
    "    wrong_tool = random.choice(incorrect_tools)\n",
    "    required_params = tool_param_templates.get(wrong_tool, [])\n",
    "\n",
    "    # Build dummy argument string\n",
    "    dummy_args = []\n",
    "    for param in required_params:\n",
    "        dummy_value = random.choice(\n",
    "            [42, 3.14, '\"example\"', \"[1, 2, 3]\", \"True\"]\n",
    "        )  # random but plausible\n",
    "        dummy_args.append(f\"{param}={dummy_value}\")\n",
    "\n",
    "    wrong_tool_call = f\"{wrong_tool}({', '.join(dummy_args)})\"\n",
    "\n",
    "    wrong_examples.append(\n",
    "        {\n",
    "            \"question\": question,\n",
    "            \"tool_call\": wrong_tool_call,\n",
    "            \"tool_definitions\": tool_definitions_text,\n",
    "        }\n",
    "    )\n",
    "\n",
    "# Create and label the correct examples\n",
    "df_eval[\"true_label\"] = \"correct\"\n",
    "\n",
    "# Create and label the incorrect examples\n",
    "df_wrong = pd.DataFrame(wrong_examples)\n",
    "df_wrong[\"true_label\"] = \"incorrect\"\n",
    "\n",
    "# Combine both datasets\n",
    "df_combined = pd.concat([df_eval, df_wrong], ignore_index=True)\n",
    "\n",
    "# OPTIONAL: Shuffle AFTER labeling\n",
    "df_eval_final = df_combined.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1BsKhwkqEGbc"
   },
   "source": [
    "## Display Agent Tool Parameter Extraction Prompt Template\n",
    "\n",
    "View the default template used to evaluate agent tool parameter extraction.\n",
    "You can tweak this template and evaluate its performance relative to the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOOL_PARAMETER_EXTRACTION_PROMPT_TEMPLATE = \"\"\"\n",
    "You are an evaluation assistant assessing whether the parameters provided in a tool call correctly\n",
    "match the user's question. Your task is to decide if the parameters selected are correct and\n",
    "sufficient to answer the question, using only the list of available tools and their parameter\n",
    "definitions provided below. You are not responsible for checking if the correct tool was selected\n",
    "— assume the tool is correct. You are evaluating **only** whether the parameters are accurate and\n",
    "justified based on the content of the question.\n",
    "\n",
    "Think like a grading rubric. Be strict. If the parameters are not clearly correct based on the\n",
    "question alone, label them \"incorrect\". Do not make assumptions or infer values that are not\n",
    "explicitly stated or directly supported by the question. Only use the information provided.\n",
    "\n",
    "Your response must be a **single word**: either `\"correct\"` or `\"incorrect\"`.\n",
    "Do not include any explanation, punctuation, or other characters. The output will be parsed\n",
    "programmatically.\n",
    "\n",
    "---\n",
    "Label the parameter extraction as `\"correct\"` if **all** of the following are true:\n",
    "- All required parameters are present and correctly filled based on the question\n",
    "- The parameter values are explicitly justified by the question\n",
    "- No extra, irrelevant, or hallucinated parameters are included\n",
    "\n",
    "Label the parameter extraction as `\"incorrect\"` if **any** of the following are true:\n",
    "- Any required parameter is missing, malformed, or incorrectly populated\n",
    "- Any parameter value is inferred or not clearly supported by the question\n",
    "- Any extra or irrelevant parameter is included\n",
    "---\n",
    "\n",
    "[BEGIN DATA]\n",
    "************\n",
    "[Question]: {question}\n",
    "************\n",
    "[Tool Called With Parameters]: {tool_call}\n",
    "************\n",
    "[END DATA]\n",
    "\n",
    "[Tool Definitions]: {tool_definitions}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oR2oNMS6EGbc"
   },
   "source": [
    "Eval template variables:\n",
    "\n",
    "- **question** : The question the user poses\n",
    "- **tool_call** : The tool that is actually called (ground truth)\n",
    "- **tool_definitions** : All the available tools to the agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tV3bLwrAEGbd"
   },
   "source": [
    "## Configure the LLM\n",
    "\n",
    "Configure your API keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dlm4HdAGEGbd",
    "outputId": "799bd4f0-ee41-4afa-aa40-4236d29514e6"
   },
   "outputs": [],
   "source": [
    "if not (openai_api_key := os.getenv(\"OPENAI_API_KEY\")):\n",
    "    openai_api_key = getpass(\"🔑 Enter your OpenAI API key: \")\n",
    "\n",
    "openai.api_key = openai_api_key\n",
    "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
    "\n",
    "\n",
    "# Set Anthropic API key\n",
    "if not (anthropic_api_key := os.getenv(\"ANTHROPIC_API_KEY\")):\n",
    "    anthropic_api_key = getpass(\"🔑 Enter your Anthropic API key: \")\n",
    "\n",
    "os.environ[\"ANTHROPIC_API_KEY\"] = anthropic_api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9NJpkArqgrmE"
   },
   "source": [
    "## Evaluate the Dataset Using Phoenix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "q5zDDCXqPwN0",
    "outputId": "2254d24a-f409-43d4-b0c2-4f481e3b560a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🧪 Evaluating OpenAI model: gpt-4o\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9391abe6f3e34887a0e38ab0ea43c509",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating Dataframe |          | 0/50 (0.0%) | ⏳ 00:00<? | ?it/s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [02:53<00:00, 173.63s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "from phoenix.evals import (\n",
    "    create_classifier,\n",
    "    evaluate_dataframe,\n",
    ")\n",
    "from phoenix.evals.llm import LLM\n",
    "\n",
    "# List of OpenAI model codes to benchmark\n",
    "open_ai_models = [\n",
    "    \"gpt-4o\",  # GPT-4 Omni (May 2024)\n",
    "    \"gpt-4o-mini\",  # Smaller version of GPT-4o\n",
    "    \"gpt-4\",  # GPT-4 (March 2023)\n",
    "    \"gpt-4-turbo\",  # GPT-4 Turbo (November 2023)\n",
    "    \"gpt-4-turbo-2024-04-09\",  # GPT-4 Turbo snapshot (April 2024)\n",
    "    \"gpt-3.5-turbo\",  # GPT-3.5 Turbo (March 2023)\n",
    "    \"gpt-3.5-turbo-0125\",  # GPT-3.5 Turbo snapshot (January 2025)\n",
    "    \"o1\",  # OpenAI's first reasoning model\n",
    "    \"o1-mini\",  # Smaller version of o1\n",
    "    \"o3\",  # Successor to o1 with improved reasoning\n",
    "    \"o3-mini\",  # Smaller version of o3\n",
    "    \"o4-mini\",  # Latest mini reasoning model (April 2025)\n",
    "]\n",
    "\n",
    "all_results = []\n",
    "\n",
    "# ---------- Evaluate OpenAI Models ----------\n",
    "for model_name in tqdm(open_ai_models):\n",
    "    print(f\"\\n🧪 Evaluating OpenAI model: {model_name}\")\n",
    "\n",
    "    model = LLM(provider=\"openai\", model=model_name)\n",
    "\n",
    "    tool_param_eval = create_classifier(\n",
    "        name=\"tool parameter extraction\",\n",
    "        prompt_template=TOOL_PARAMETER_EXTRACTION_PROMPT_TEMPLATE,\n",
    "        llm=model,\n",
    "        choices={\"correct\": 1.0, \"incorrect\": 0.0},\n",
    "    )\n",
    "\n",
    "    results_df = evaluate_dataframe(dataframe=df_eval, evaluators=[tool_param_eval])\n",
    "\n",
    "    results_df[\"label\"] = results_df[\"tool parameter extraction_score\"].apply(\n",
    "        lambda x: x.get(\"label\") if isinstance(x, dict) else None\n",
    "    )\n",
    "    results_df[\"model\"] = results_df[\"tool parameter extraction_score\"].apply(\n",
    "        lambda x: x.get(\"metadata\").get(\"model\") if isinstance(x, dict) else None\n",
    "    )\n",
    "    all_results.append(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sNF3fP4QcFs7"
   },
   "source": [
    "## Evaluate Anthropic Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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      "abba9e1a7ad34e239636d287c9230c90",
      "5f52ca57a62e4aa485740e36552fe629"
     ]
    },
    "id": "LJX1KXX2cE_v",
    "outputId": "0c625656-1d00-4aaa-b7f1-ea0a7b15a3e0"
   },
   "outputs": [],
   "source": [
    "# ---------- Evaluate Anthropic Models ----------\n",
    "\n",
    "# List of Claude (Anthropic) models to benchmark\n",
    "anthropic_models = [\n",
    "    \"claude-3-opus-20240229\",  # Claude 3 Opus\n",
    "    \"claude-3-sonnet-20240229\",  # Claude 3 Sonnet\n",
    "    \"claude-3-haiku-20240307\",  # Claude 3 Haiku\n",
    "    \"claude-opus-4-20250514\",  # Claude Opus 4\n",
    "    \"claude-sonnet-4-20250514\",  # Claude Sonnet 4\n",
    "]\n",
    "\n",
    "\n",
    "for model_name in tqdm(anthropic_models):\n",
    "    print(f\"\\n🧪 Evaluating Anthropic model: {model_name}\")\n",
    "\n",
    "    model = LLM(provider=\"anthropic\", model=model_name)\n",
    "\n",
    "    tool_param_eval = create_classifier(\n",
    "        name=\"tool parameter extraction\",\n",
    "        prompt_template=TOOL_PARAMETER_EXTRACTION_PROMPT_TEMPLATE,\n",
    "        llm=model,\n",
    "        choices={\"correct\": 1.0, \"incorrect\": 0.0},\n",
    "    )\n",
    "\n",
    "    results_df = evaluate_dataframe(dataframe=df_eval, evaluators=[tool_param_eval])\n",
    "\n",
    "    results_df[\"label\"] = results_df[\"tool parameter extraction_score\"].apply(\n",
    "        lambda x: x.get(\"label\") if isinstance(x, dict) else None\n",
    "    )\n",
    "    results_df[\"model\"] = results_df[\"tool parameter extraction_score\"].apply(\n",
    "        lambda x: x.get(\"metadata\").get(\"model\") if isinstance(x, dict) else None\n",
    "    )\n",
    "    all_results.append(results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z7W3RD-yUrvR"
   },
   "source": [
    "## Show Results\n",
    "\n",
    "Let's see how well different models performed as a Judge! We'll show:\n",
    "- Confusion Matrix for one model (GPT 4o)\n",
    "- F1 score per model benchmarked, to compare how different models perform as a judge vs the ground truth\n",
    "-  An agreement matrix to see which models agree with each other as judges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "MEZEr0cVUu9Z",
    "outputId": "811f7d62-4742-43f7-9aef-8eec539cf47c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gg/2586gdl96294y9xs_3j3l7t00000gp/T/ipykernel_75032/1014510063.py:49: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pycm/utils.py:360: RuntimeWarning: Specified classes are not a subset of the classes in the actual and predicted vectors.\n",
      "  warn(CLASSES_WARNING, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "\n",
    "# Combine results and save\n",
    "df_all_models = pd.concat(all_results, ignore_index=True)\n",
    "df_all_models.to_csv(\"judge_results_all_models.csv\", index=False)\n",
    "\n",
    "# Compute metrics\n",
    "metrics = []\n",
    "models = df_all_models[\"model\"].unique()\n",
    "\n",
    "for model_name in models:\n",
    "    df_model = df_all_models[df_all_models[\"model\"] == model_name]\n",
    "\n",
    "    # Keep only valid predictions and labels\n",
    "    df_model = df_model[\n",
    "        df_model[\"label\"].isin([\"correct\", \"incorrect\"])\n",
    "        & df_model[\"true_label\"].isin([\"correct\", \"incorrect\"])\n",
    "    ]\n",
    "\n",
    "    # Use real ground truth, given we added some incorrect examples\n",
    "    true_labels = df_model[\"true_label\"].tolist()\n",
    "    pred_labels = df_model[\"label\"].tolist()\n",
    "\n",
    "    accuracy = accuracy_score(true_labels, pred_labels)\n",
    "    precision = precision_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
    "    recall = recall_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
    "    f1 = f1_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
    "\n",
    "    metrics.append(\n",
    "        {\n",
    "            \"Model\": model_name,\n",
    "            \"Accuracy\": accuracy,\n",
    "            \"Precision\": precision,\n",
    "            \"Recall\": recall,\n",
    "            \"F1 Score\": f1,\n",
    "        }\n",
    "    )\n",
    "\n",
    "# Create dataframe and sort it\n",
    "df_metrics = pd.DataFrame(metrics)\n",
    "df_metrics_sorted = df_metrics.sort_values(\"F1 Score\", ascending=False)\n",
    "\n",
    "# Preserve order of models for plotting\n",
    "model_order = df_metrics_sorted[\"Model\"].tolist()\n",
    "\n",
    "# Plot with fixed order\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(\n",
    "    data=df_metrics_sorted, x=\"F1 Score\", y=\"Model\", palette=\"Blues_d\", order=model_order\n",
    ")\n",
    "plt.title(\"F1 Score by Model\")\n",
    "plt.xlim(0, 1)\n",
    "\n",
    "# Add labels at correct y-axis positions\n",
    "for i, model in enumerate(model_order):\n",
    "    score = df_metrics_sorted[df_metrics_sorted[\"Model\"] == model][\"F1 Score\"].values[0]\n",
    "    ax.text(score + 0.01, i, f\"{score:.2f}\", va=\"center\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---- Generate agreement Matrix for models -------\n",
    "\n",
    "# Generate a consistent ID for each example\n",
    "df_all_models[\"example_id\"] = df_all_models.groupby(\"model\").cumcount()\n",
    "\n",
    "# Pivot to wide format: one row per example, one column per model\n",
    "df_pivot = df_all_models.pivot(index=\"example_id\", columns=\"model\", values=\"label\")\n",
    "\n",
    "# Compute agreement rates between models\n",
    "agreement_matrix = pd.DataFrame(index=model_order, columns=model_order)\n",
    "\n",
    "for m1 in model_order:\n",
    "    for m2 in model_order:\n",
    "        agreement = (df_pivot[m1] == df_pivot[m2]).mean()\n",
    "        agreement_matrix.loc[m1, m2] = agreement\n",
    "\n",
    "agreement_matrix = agreement_matrix.astype(float)\n",
    "\n",
    "# ----------------------------------------\n",
    "# Plot Agreement Heatmap\n",
    "# ----------------------------------------\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(agreement_matrix, annot=True, cmap=\"Blues\", fmt=\".2f\", cbar=True)\n",
    "plt.title(\"Model Agreement Matrix (Label Agreement Rate)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---------------- Confusion Matrix for GPT-4o ----------------\n",
    "\n",
    "# Filter to GPT-4o predictions and valid labels\n",
    "df_4o = df_all_models[\n",
    "    (df_all_models[\"model\"] == \"gpt-4o\")\n",
    "    & (df_all_models[\"label\"].isin([\"correct\", \"incorrect\"]))\n",
    "    & (df_all_models[\"true_label\"].isin([\"correct\", \"incorrect\"]))\n",
    "]\n",
    "\n",
    "true_labels_4o = df_4o[\"true_label\"].tolist()\n",
    "pred_labels_4o = df_4o[\"label\"].tolist()\n",
    "rails = [\"correct\", \"incorrect\"]  # class order\n",
    "\n",
    "# Generate and plot confusion matrix\n",
    "confusion_matrix_4o = ConfusionMatrix(\n",
    "    actual_vector=true_labels_4o,\n",
    "    predict_vector=pred_labels_4o,\n",
    "    classes=rails,\n",
    ")\n",
    "confusion_matrix_4o.plot(\n",
    "    cmap=plt.colormaps[\"Blues\"],\n",
    "    number_label=True,\n",
    "    normalized=True,\n",
    ")\n",
    "plt.title(\"GPT-4o Confusion Matrix\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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